Overview

Dataset statistics

Number of variables24
Number of observations55314
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 MiB
Average record size in memory188.0 B

Variable types

Categorical4
Numeric19
Text1

Alerts

season has constant value ""Constant
club_id is highly overall correlated with league_id and 2 other fieldsHigh correlation
league_id is highly overall correlated with club_id and 3 other fieldsHigh correlation
cohort_season is highly overall correlated with club_id and 2 other fieldsHigh correlation
avg_stars_top_11_players is highly overall correlated with league_id and 8 other fieldsHigh correlation
avg_stars_top_14_players is highly overall correlated with league_id and 8 other fieldsHigh correlation
days_active_last_28_days is highly overall correlated with avg_stars_top_11_players and 9 other fieldsHigh correlation
league_match_watched_count_last_28_days is highly overall correlated with avg_stars_top_11_players and 9 other fieldsHigh correlation
session_count_last_28_days is highly overall correlated with avg_stars_top_11_players and 9 other fieldsHigh correlation
playtime_last_28_days is highly overall correlated with avg_stars_top_11_players and 9 other fieldsHigh correlation
league_match_won_count_last_28_days is highly overall correlated with days_active_last_28_days and 4 other fieldsHigh correlation
training_count_last_28_days is highly overall correlated with avg_stars_top_11_players and 9 other fieldsHigh correlation
global_competition_level is highly overall correlated with club_id and 9 other fieldsHigh correlation
tokens_spent_last_28_days is highly overall correlated with avg_stars_top_11_players and 7 other fieldsHigh correlation
rests_stash is highly overall correlated with morale_boosters_stashHigh correlation
morale_boosters_stash is highly overall correlated with rests_stashHigh correlation
league_rank is highly overall correlated with days_active_last_28_days and 4 other fieldsHigh correlation
registration_platform_specific is highly imbalanced (51.4%)Imbalance
tokens_spent_last_28_days is highly skewed (γ1 = 75.32770805)Skewed
tokens_stash is highly skewed (γ1 = 230.8711493)Skewed
club_id has unique valuesUnique
avg_training_factor_top_11_players has unique valuesUnique
days_active_last_28_days has 13005 (23.5%) zerosZeros
league_match_watched_count_last_28_days has 29504 (53.3%) zerosZeros
session_count_last_28_days has 13029 (23.6%) zerosZeros
playtime_last_28_days has 13029 (23.6%) zerosZeros
training_count_last_28_days has 19207 (34.7%) zerosZeros
global_competition_level has 17551 (31.7%) zerosZeros
tokens_spent_last_28_days has 18799 (34.0%) zerosZeros
tokens_stash has 1087 (2.0%) zerosZeros

Reproduction

Analysis started2023-11-19 15:54:47.701388
Analysis finished2023-11-19 15:57:50.107456
Duration3 minutes and 2.41 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

season
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size432.3 KiB
173
55314 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters165942
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row173
2nd row173
3rd row173
4th row173
5th row173

Common Values

ValueCountFrequency (%)
173 55314
100.0%

Length

2023-11-19T16:57:50.409458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T16:57:50.796326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
173 55314
100.0%

Most occurring characters

ValueCountFrequency (%)
1 55314
33.3%
7 55314
33.3%
3 55314
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 165942
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55314
33.3%
7 55314
33.3%
3 55314
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 165942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55314
33.3%
7 55314
33.3%
3 55314
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55314
33.3%
7 55314
33.3%
3 55314
33.3%

club_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12109765
Minimum275
Maximum14514770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:51.535050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum275
5-th percentile5993755.9
Q111511047
median13928216
Q314244540
95-th percentile14409146
Maximum14514770
Range14514495
Interquartile range (IQR)2733492.5

Descriptive statistics

Standard deviation3384016.2
Coefficient of variation (CV)0.27944523
Kurtosis1.6704162
Mean12109765
Median Absolute Deviation (MAD)439454.5
Skewness-1.6330064
Sum6.6983956 × 1011
Variance1.1451566 × 1013
MonotonicityNot monotonic
2023-11-19T16:57:52.218629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6042825 1
 
< 0.1%
14347346 1
 
< 0.1%
6044120 1
 
< 0.1%
13607777 1
 
< 0.1%
14195413 1
 
< 0.1%
13991054 1
 
< 0.1%
11852863 1
 
< 0.1%
14166575 1
 
< 0.1%
6032412 1
 
< 0.1%
13712821 1
 
< 0.1%
Other values (55304) 55304
> 99.9%
ValueCountFrequency (%)
275 1
< 0.1%
16371 1
< 0.1%
22726 1
< 0.1%
27248 1
< 0.1%
43766 1
< 0.1%
48258 1
< 0.1%
68761 1
< 0.1%
110893 1
< 0.1%
120096 1
< 0.1%
121182 1
< 0.1%
ValueCountFrequency (%)
14514770 1
< 0.1%
14514423 1
< 0.1%
14514347 1
< 0.1%
14513703 1
< 0.1%
14513382 1
< 0.1%
14513043 1
< 0.1%
14513001 1
< 0.1%
14512908 1
< 0.1%
14512802 1
< 0.1%
14512708 1
< 0.1%

league_id
Real number (ℝ)

HIGH CORRELATION 

Distinct3951
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2909804.2
Minimum2904743
Maximum2912940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:52.815495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2904743
5-th percentile2906496
Q12907961
median2910038
Q32911697
95-th percentile2912729
Maximum2912940
Range8197
Interquartile range (IQR)3736

Descriptive statistics

Standard deviation2102.1399
Coefficient of variation (CV)0.00072243346
Kurtosis-1.2026611
Mean2909804.2
Median Absolute Deviation (MAD)1825
Skewness-0.22744403
Sum1.6095291 × 1011
Variance4418992.3
MonotonicityNot monotonic
2023-11-19T16:57:53.307496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2912348 14
 
< 0.1%
2908518 14
 
< 0.1%
2912576 14
 
< 0.1%
2910101 14
 
< 0.1%
2911527 14
 
< 0.1%
2911848 14
 
< 0.1%
2907339 14
 
< 0.1%
2911991 14
 
< 0.1%
2912455 14
 
< 0.1%
2912312 14
 
< 0.1%
Other values (3941) 55174
99.7%
ValueCountFrequency (%)
2904743 14
< 0.1%
2904844 14
< 0.1%
2904897 14
< 0.1%
2904917 14
< 0.1%
2905007 14
< 0.1%
2905169 14
< 0.1%
2905175 14
< 0.1%
2905181 14
< 0.1%
2905225 14
< 0.1%
2905228 14
< 0.1%
ValueCountFrequency (%)
2912940 14
< 0.1%
2912939 14
< 0.1%
2912938 14
< 0.1%
2912936 14
< 0.1%
2912935 14
< 0.1%
2912934 14
< 0.1%
2912933 14
< 0.1%
2912932 14
< 0.1%
2912931 14
< 0.1%
2912930 14
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size432.3 KiB
0) NonPayer
41843 
2) Minnow
6047 
1) ExPayer
5025 
3) Dolphin
 
1854
4) Whale
 
545

Length

Max length11
Median length11
Mean length10.627436
Min length8

Characters and Unicode

Total characters587846
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2) Minnow
2nd row2) Minnow
3rd row4) Whale
4th row0) NonPayer
5th row0) NonPayer

Common Values

ValueCountFrequency (%)
0) NonPayer 41843
75.6%
2) Minnow 6047
 
10.9%
1) ExPayer 5025
 
9.1%
3) Dolphin 1854
 
3.4%
4) Whale 545
 
1.0%

Length

2023-11-19T16:57:53.775496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T16:57:54.258767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41843
37.8%
nonpayer 41843
37.8%
2 6047
 
5.5%
minnow 6047
 
5.5%
1 5025
 
4.5%
expayer 5025
 
4.5%
3 1854
 
1.7%
dolphin 1854
 
1.7%
4 545
 
0.5%
whale 545
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 55791
9.5%
55314
9.4%
) 55314
9.4%
o 49744
8.5%
a 47413
8.1%
e 47413
8.1%
P 46868
8.0%
y 46868
8.0%
r 46868
8.0%
0 41843
7.1%
Other values (15) 94410
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 319722
54.4%
Uppercase Letter 102182
 
17.4%
Space Separator 55314
 
9.4%
Close Punctuation 55314
 
9.4%
Decimal Number 55314
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 55791
17.4%
o 49744
15.6%
a 47413
14.8%
e 47413
14.8%
y 46868
14.7%
r 46868
14.7%
i 7901
 
2.5%
w 6047
 
1.9%
x 5025
 
1.6%
l 2399
 
0.8%
Other values (2) 4253
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
P 46868
45.9%
N 41843
40.9%
M 6047
 
5.9%
E 5025
 
4.9%
D 1854
 
1.8%
W 545
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 41843
75.6%
2 6047
 
10.9%
1 5025
 
9.1%
3 1854
 
3.4%
4 545
 
1.0%
Space Separator
ValueCountFrequency (%)
55314
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 421904
71.8%
Common 165942
 
28.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 55791
13.2%
o 49744
11.8%
a 47413
11.2%
e 47413
11.2%
P 46868
11.1%
y 46868
11.1%
r 46868
11.1%
N 41843
9.9%
i 7901
 
1.9%
w 6047
 
1.4%
Other values (8) 25148
6.0%
Common
ValueCountFrequency (%)
55314
33.3%
) 55314
33.3%
0 41843
25.2%
2 6047
 
3.6%
1 5025
 
3.0%
3 1854
 
1.1%
4 545
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 55791
9.5%
55314
9.4%
) 55314
9.4%
o 49744
8.5%
a 47413
8.1%
e 47413
8.1%
P 46868
8.0%
y 46868
8.0%
r 46868
8.0%
0 41843
7.1%
Other values (15) 94410
16.1%

cohort_season
Real number (ℝ)

HIGH CORRELATION 

Distinct172
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.409372
Minimum1
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:54.785857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q317
95-th percentile135
Maximum172
Range171
Interquartile range (IQR)15

Descriptive statistics

Standard deviation42.009274
Coefficient of variation (CV)1.7945494
Kurtosis3.2492578
Mean23.409372
Median Absolute Deviation (MAD)3
Skewness2.1340015
Sum1294866
Variance1764.7791
MonotonicityNot monotonic
2023-11-19T16:57:55.440102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12333
22.3%
1 8529
15.4%
3 6409
 
11.6%
4 3320
 
6.0%
5 2014
 
3.6%
6 1452
 
2.6%
7 1204
 
2.2%
8 1020
 
1.8%
11 817
 
1.5%
12 801
 
1.4%
Other values (162) 17415
31.5%
ValueCountFrequency (%)
1 8529
15.4%
2 12333
22.3%
3 6409
11.6%
4 3320
 
6.0%
5 2014
 
3.6%
6 1452
 
2.6%
7 1204
 
2.2%
8 1020
 
1.8%
9 793
 
1.4%
10 713
 
1.3%
ValueCountFrequency (%)
172 27
 
< 0.1%
171 32
 
0.1%
170 49
0.1%
169 46
0.1%
168 23
 
< 0.1%
167 58
0.1%
166 100
0.2%
165 50
0.1%
164 45
0.1%
163 66
0.1%

avg_age_top_11_players
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.718661
Minimum18
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:55.888101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q122
median23
Q325
95-th percentile28
Maximum33
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2062402
Coefficient of variation (CV)0.093017065
Kurtosis1.8597279
Mean23.718661
Median Absolute Deviation (MAD)1
Skewness0.96748057
Sum1311974
Variance4.8674959
MonotonicityNot monotonic
2023-11-19T16:57:56.314101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
23 11528
20.8%
24 10856
19.6%
22 9102
16.5%
25 7455
13.5%
21 5023
9.1%
26 4028
 
7.3%
27 2091
 
3.8%
20 1851
 
3.3%
28 1079
 
2.0%
29 627
 
1.1%
Other values (6) 1674
 
3.0%
ValueCountFrequency (%)
18 16
 
< 0.1%
19 345
 
0.6%
20 1851
 
3.3%
21 5023
9.1%
22 9102
16.5%
23 11528
20.8%
24 10856
19.6%
25 7455
13.5%
26 4028
 
7.3%
27 2091
 
3.8%
ValueCountFrequency (%)
33 165
 
0.3%
32 254
 
0.5%
31 348
 
0.6%
30 546
 
1.0%
29 627
 
1.1%
28 1079
 
2.0%
27 2091
 
3.8%
26 4028
 
7.3%
25 7455
13.5%
24 10856
19.6%

avg_stars_top_11_players
Real number (ℝ)

HIGH CORRELATION 

Distinct55260
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5335157
Minimum1.451697
Maximum23.716073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:56.815101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.451697
5-th percentile2.7761788
Q13.6911364
median4.4813758
Q35.2173939
95-th percentile6.6650405
Maximum23.716073
Range22.264376
Interquartile range (IQR)1.5262575

Descriptive statistics

Standard deviation1.2047528
Coefficient of variation (CV)0.26574361
Kurtosis2.7556137
Mean4.5335157
Median Absolute Deviation (MAD)0.76335147
Skewness0.69560239
Sum250766.89
Variance1.4514294
MonotonicityNot monotonic
2023-11-19T16:57:57.414104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.277163632 2
 
< 0.1%
4.377672734 2
 
< 0.1%
4.429527255 2
 
< 0.1%
4.627006115 2
 
< 0.1%
4.370133316 2
 
< 0.1%
3.710533318 2
 
< 0.1%
4.577648519 2
 
< 0.1%
4.841284864 2
 
< 0.1%
3.749309091 2
 
< 0.1%
2.775151512 2
 
< 0.1%
Other values (55250) 55294
> 99.9%
ValueCountFrequency (%)
1.451696972 1
< 0.1%
1.831236363 1
< 0.1%
1.854957567 1
< 0.1%
1.865236365 1
< 0.1%
1.876206067 1
< 0.1%
1.896557572 1
< 0.1%
1.899745441 1
< 0.1%
1.901660603 1
< 0.1%
1.904569691 1
< 0.1%
1.906545455 1
< 0.1%
ValueCountFrequency (%)
23.71607281 1
< 0.1%
20.13309084 1
< 0.1%
17.55141809 1
< 0.1%
16.25966042 1
< 0.1%
14.95066665 1
< 0.1%
14.65561202 1
< 0.1%
14.54728492 1
< 0.1%
13.11088482 1
< 0.1%
11.32044811 1
< 0.1%
11.10578199 1
< 0.1%

avg_stars_top_14_players
Real number (ℝ)

HIGH CORRELATION 

Distinct55282
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3447756
Minimum1.3549048
Maximum20.434876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:57:58.000567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3549048
5-th percentile2.6438814
Q13.5000619
median4.3115905
Q35.0217262
95-th percentile6.4125831
Maximum20.434876
Range19.079971
Interquartile range (IQR)1.5216643

Descriptive statistics

Standard deviation1.1770682
Coefficient of variation (CV)0.27091575
Kurtosis1.8476815
Mean4.3447756
Median Absolute Deviation (MAD)0.75753812
Skewness0.62752189
Sum240326.92
Variance1.3854894
MonotonicityNot monotonic
2023-11-19T16:57:58.560569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.127152376 2
 
< 0.1%
2.958314287 2
 
< 0.1%
3.725733316 2
 
< 0.1%
4.907609544 2
 
< 0.1%
2.837742855 2
 
< 0.1%
2.683171424 2
 
< 0.1%
3.900600006 2
 
< 0.1%
4.866428586 2
 
< 0.1%
4.236361914 2
 
< 0.1%
2.791199997 2
 
< 0.1%
Other values (55272) 55294
> 99.9%
ValueCountFrequency (%)
1.354904763 1
< 0.1%
1.717933317 1
< 0.1%
1.746590481 1
< 0.1%
1.752380952 1
< 0.1%
1.758390477 1
< 0.1%
1.762361903 1
< 0.1%
1.768742851 1
< 0.1%
1.769571429 1
< 0.1%
1.778276186 1
< 0.1%
1.793047624 1
< 0.1%
ValueCountFrequency (%)
20.43487619 1
< 0.1%
17.90579995 1
< 0.1%
15.90664743 1
< 0.1%
14.71906657 1
< 0.1%
14.43303814 1
< 0.1%
13.66381894 1
< 0.1%
12.56584757 1
< 0.1%
12.42757141 1
< 0.1%
10.80125686 1
< 0.1%
10.51113339 1
< 0.1%

avg_training_factor_top_11_players
Real number (ℝ)

UNIQUE 

Distinct55314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58437182
Minimum-1.2877365
Maximum5.1644943
Zeros0
Zeros (%)0.0%
Negative132
Negative (%)0.2%
Memory size432.3 KiB
2023-11-19T16:57:59.099567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2877365
5-th percentile0.39617839
Q10.47957092
median0.56115796
Q30.64817985
95-th percentile0.90088122
Maximum5.1644943
Range6.4522308
Interquartile range (IQR)0.16860893

Descriptive statistics

Standard deviation0.17808997
Coefficient of variation (CV)0.30475455
Kurtosis39.805591
Mean0.58437182
Median Absolute Deviation (MAD)0.083941911
Skewness3.1056679
Sum32323.943
Variance0.031716038
MonotonicityNot monotonic
2023-11-19T16:57:59.765305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.778801246 1
 
< 0.1%
0.4269814922 1
 
< 0.1%
0.5223622933 1
 
< 0.1%
0.7602145878 1
 
< 0.1%
0.514817867 1
 
< 0.1%
0.6951656966 1
 
< 0.1%
0.6121318988 1
 
< 0.1%
0.4347173591 1
 
< 0.1%
0.438736348 1
 
< 0.1%
0.5855279554 1
 
< 0.1%
Other values (55304) 55304
> 99.9%
ValueCountFrequency (%)
-1.287736497 1
< 0.1%
-0.8862160754 1
< 0.1%
-0.8436819335 1
< 0.1%
-0.8364241275 1
< 0.1%
-0.7575517528 1
< 0.1%
-0.7369245558 1
< 0.1%
-0.7369205118 1
< 0.1%
-0.7149970649 1
< 0.1%
-0.6670450834 1
< 0.1%
-0.6630123572 1
< 0.1%
ValueCountFrequency (%)
5.164494312 1
< 0.1%
4.834535193 1
< 0.1%
3.999635499 1
< 0.1%
3.875257411 1
< 0.1%
3.562516136 1
< 0.1%
3.550029568 1
< 0.1%
3.364479495 1
< 0.1%
3.311070816 1
< 0.1%
3.253493218 1
< 0.1%
3.182084713 1
< 0.1%

days_active_last_28_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.357089
Minimum0
Maximum28
Zeros13005
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:00.604402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q327
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)26

Descriptive statistics

Standard deviation11.849102
Coefficient of variation (CV)0.95889109
Kurtosis-1.7083818
Mean12.357089
Median Absolute Deviation (MAD)7
Skewness0.27544921
Sum683520
Variance140.40122
MonotonicityNot monotonic
2023-11-19T16:58:01.275886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 13005
23.5%
28 11952
21.6%
1 5064
 
9.2%
2 2961
 
5.4%
27 2603
 
4.7%
3 1993
 
3.6%
26 1551
 
2.8%
4 1529
 
2.8%
5 1224
 
2.2%
25 1157
 
2.1%
Other values (19) 12275
22.2%
ValueCountFrequency (%)
0 13005
23.5%
1 5064
 
9.2%
2 2961
 
5.4%
3 1993
 
3.6%
4 1529
 
2.8%
5 1224
 
2.2%
6 1017
 
1.8%
7 872
 
1.6%
8 790
 
1.4%
9 732
 
1.3%
ValueCountFrequency (%)
28 11952
21.6%
27 2603
 
4.7%
26 1551
 
2.8%
25 1157
 
2.1%
24 877
 
1.6%
23 770
 
1.4%
22 656
 
1.2%
21 625
 
1.1%
20 566
 
1.0%
19 547
 
1.0%

league_match_watched_count_last_28_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6231695
Minimum0
Maximum26
Zeros29504
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:01.723885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile18
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.9060546
Coefficient of variation (CV)1.6300796
Kurtosis2.1572043
Mean3.6231695
Median Absolute Deviation (MAD)0
Skewness1.7593748
Sum200412
Variance34.881481
MonotonicityNot monotonic
2023-11-19T16:58:02.202886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 29504
53.3%
1 4759
 
8.6%
2 2790
 
5.0%
3 2007
 
3.6%
4 1642
 
3.0%
5 1390
 
2.5%
6 1287
 
2.3%
7 1087
 
2.0%
8 1046
 
1.9%
9 983
 
1.8%
Other values (17) 8819
 
15.9%
ValueCountFrequency (%)
0 29504
53.3%
1 4759
 
8.6%
2 2790
 
5.0%
3 2007
 
3.6%
4 1642
 
3.0%
5 1390
 
2.5%
6 1287
 
2.3%
7 1087
 
2.0%
8 1046
 
1.9%
9 983
 
1.8%
ValueCountFrequency (%)
26 64
 
0.1%
25 137
 
0.2%
24 218
 
0.4%
23 271
0.5%
22 299
0.5%
21 389
0.7%
20 434
0.8%
19 497
0.9%
18 566
1.0%
17 575
1.0%

session_count_last_28_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct732
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.432169
Minimum0
Maximum1365
Zeros13029
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:02.767102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median16
Q389
95-th percentile263
Maximum1365
Range1365
Interquartile range (IQR)88

Descriptive statistics

Standard deviation101.83167
Coefficient of variation (CV)1.6053632
Kurtosis14.254607
Mean63.432169
Median Absolute Deviation (MAD)16
Skewness2.9778055
Sum3508687
Variance10369.689
MonotonicityNot monotonic
2023-11-19T16:58:03.430189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13029
23.6%
1 3457
 
6.2%
2 2196
 
4.0%
3 1406
 
2.5%
4 1156
 
2.1%
5 925
 
1.7%
6 818
 
1.5%
7 702
 
1.3%
8 627
 
1.1%
9 563
 
1.0%
Other values (722) 30435
55.0%
ValueCountFrequency (%)
0 13029
23.6%
1 3457
 
6.2%
2 2196
 
4.0%
3 1406
 
2.5%
4 1156
 
2.1%
5 925
 
1.7%
6 818
 
1.5%
7 702
 
1.3%
8 627
 
1.1%
9 563
 
1.0%
ValueCountFrequency (%)
1365 1
< 0.1%
1342 1
< 0.1%
1291 1
< 0.1%
1279 1
< 0.1%
1275 1
< 0.1%
1188 1
< 0.1%
1173 1
< 0.1%
1162 1
< 0.1%
1158 1
< 0.1%
1127 1
< 0.1%

playtime_last_28_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42244
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29551777
Minimum0
Maximum1.3404787 × 109
Zeros13029
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:04.184554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173641.5
median6230431
Q337687108
95-th percentile1.3119349 × 108
Maximum1.3404787 × 109
Range1.3404787 × 109
Interquartile range (IQR)37613466

Descriptive statistics

Standard deviation52338916
Coefficient of variation (CV)1.771092
Kurtosis33.917358
Mean29551777
Median Absolute Deviation (MAD)6230431
Skewness3.9566874
Sum1.634627 × 1012
Variance2.7393622 × 1015
MonotonicityNot monotonic
2023-11-19T16:58:05.009823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13029
 
23.6%
104338 2
 
< 0.1%
1158639 2
 
< 0.1%
6833560 2
 
< 0.1%
7829149 2
 
< 0.1%
1262716 2
 
< 0.1%
1313074 2
 
< 0.1%
36397933 2
 
< 0.1%
543178 2
 
< 0.1%
329955 2
 
< 0.1%
Other values (42234) 42267
76.4%
ValueCountFrequency (%)
0 13029
23.6%
894 1
 
< 0.1%
2103 1
 
< 0.1%
2569 2
 
< 0.1%
2633 1
 
< 0.1%
2658 1
 
< 0.1%
2706 1
 
< 0.1%
2790 1
 
< 0.1%
3059 1
 
< 0.1%
3085 1
 
< 0.1%
ValueCountFrequency (%)
1340478682 1
< 0.1%
1310266523 1
< 0.1%
884749571 1
< 0.1%
859296125 1
< 0.1%
775760071 1
< 0.1%
766103461 1
< 0.1%
703368228 1
< 0.1%
700369247 1
< 0.1%
674335478 1
< 0.1%
673310929 1
< 0.1%
Distinct186
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:05.865462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length7.9717612
Min length4

Characters and Unicode

Total characters440950
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowPortugal
2nd rowTurkey
3rd rowBelgium
4th rowMalaysia
5th rowItaly
ValueCountFrequency (%)
indonesia 9830
 
15.7%
turkey 4563
 
7.3%
brazil 3080
 
4.9%
united 3055
 
4.9%
germany 2722
 
4.4%
france 2144
 
3.4%
thailand 2096
 
3.4%
kingdom 1924
 
3.1%
italy 1872
 
3.0%
serbia 1589
 
2.5%
Other values (208) 29664
47.4%
2023-11-19T16:58:07.228860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55535
 
12.6%
n 47181
 
10.7%
e 41654
 
9.4%
i 39382
 
8.9%
r 25656
 
5.8%
d 22354
 
5.1%
o 22319
 
5.1%
s 16204
 
3.7%
l 16030
 
3.6%
t 13416
 
3.0%
Other values (44) 141219
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 371690
84.3%
Uppercase Letter 61628
 
14.0%
Space Separator 7225
 
1.6%
Other Punctuation 406
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55535
14.9%
n 47181
12.7%
e 41654
11.2%
i 39382
10.6%
r 25656
 
6.9%
d 22354
 
6.0%
o 22319
 
6.0%
s 16204
 
4.4%
l 16030
 
4.3%
t 13416
 
3.6%
Other values (16) 71959
19.4%
Uppercase Letter
ValueCountFrequency (%)
I 13149
21.3%
T 7004
11.4%
S 5908
9.6%
B 4718
 
7.7%
G 4103
 
6.7%
U 3695
 
6.0%
A 2875
 
4.7%
F 2722
 
4.4%
R 2572
 
4.2%
P 2565
 
4.2%
Other values (14) 12317
20.0%
Other Punctuation
ValueCountFrequency (%)
, 301
74.1%
' 105
 
25.9%
Space Separator
ValueCountFrequency (%)
7225
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 433318
98.3%
Common 7632
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55535
12.8%
n 47181
 
10.9%
e 41654
 
9.6%
i 39382
 
9.1%
r 25656
 
5.9%
d 22354
 
5.2%
o 22319
 
5.2%
s 16204
 
3.7%
l 16030
 
3.7%
t 13416
 
3.1%
Other values (40) 133587
30.8%
Common
ValueCountFrequency (%)
7225
94.7%
, 301
 
3.9%
' 105
 
1.4%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55535
 
12.6%
n 47181
 
10.7%
e 41654
 
9.4%
i 39382
 
8.9%
r 25656
 
5.8%
d 22354
 
5.1%
o 22319
 
5.1%
s 16204
 
3.7%
l 16030
 
3.6%
t 13416
 
3.0%
Other values (44) 141219
32.0%

registration_platform_specific
Categorical

IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size432.3 KiB
Android Phone
35859 
iOS Phone
9896 
Flash FB Canvas
4340 
Android Tablet
 
1867
iOS Tablet
 
1214
Other values (6)
 
2138

Length

Max length24
Median length13
Mean length12.541545
Min length9

Characters and Unicode

Total characters693723
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlash FB Canvas
2nd rowAndroid Phone
3rd rowFlash FB Canvas
4th rowAndroid Phone
5th rowiOS Phone

Common Values

ValueCountFrequency (%)
Android Phone 35859
64.8%
iOS Phone 9896
 
17.9%
Flash FB Canvas 4340
 
7.8%
Android Tablet 1867
 
3.4%
iOS Tablet 1214
 
2.2%
UniversalWindows PC 724
 
1.3%
WebGL FB Canvas 702
 
1.3%
WebGL TE Site 489
 
0.9%
WebGL or Flash FB Canvas 129
 
0.2%
Flash TE Site 77
 
0.1%

Length

2023-11-19T16:58:07.770859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phone 45755
39.2%
android 37726
32.3%
ios 11110
 
9.5%
fb 5171
 
4.4%
canvas 5171
 
4.4%
flash 4563
 
3.9%
tablet 3081
 
2.6%
webgl 1337
 
1.1%
universalwindows 724
 
0.6%
pc 724
 
0.6%
Other values (3) 1312
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 90100
13.0%
o 84351
12.2%
d 76176
11.0%
61360
8.8%
e 51480
7.4%
i 50867
7.3%
h 50318
7.3%
P 46479
6.7%
r 38596
 
5.6%
A 37726
 
5.4%
Other values (18) 106270
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 494849
71.3%
Uppercase Letter 137514
 
19.8%
Space Separator 61360
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 90100
18.2%
o 84351
17.0%
d 76176
15.4%
e 51480
10.4%
i 50867
10.3%
h 50318
10.2%
r 38596
7.8%
a 18710
 
3.8%
s 11182
 
2.3%
l 8368
 
1.7%
Other values (4) 14701
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
P 46479
33.8%
A 37726
27.4%
S 11693
 
8.5%
O 11110
 
8.1%
F 9734
 
7.1%
C 5895
 
4.3%
B 5171
 
3.8%
T 3664
 
2.7%
W 2061
 
1.5%
G 1337
 
1.0%
Other values (3) 2644
 
1.9%
Space Separator
ValueCountFrequency (%)
61360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 632363
91.2%
Common 61360
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 90100
14.2%
o 84351
13.3%
d 76176
12.0%
e 51480
8.1%
i 50867
8.0%
h 50318
8.0%
P 46479
7.4%
r 38596
6.1%
A 37726
6.0%
a 18710
 
3.0%
Other values (17) 87560
13.8%
Common
ValueCountFrequency (%)
61360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 693723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 90100
13.0%
o 84351
12.2%
d 76176
11.0%
61360
8.8%
e 51480
7.4%
i 50867
7.3%
h 50318
7.3%
P 46479
6.7%
r 38596
 
5.6%
A 37726
 
5.4%
Other values (18) 106270
15.3%

league_match_won_count_last_28_days
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.411813
Minimum0
Maximum26
Zeros410
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:08.257857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q317
95-th percentile24
Maximum26
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.4030132
Coefficient of variation (CV)0.51588059
Kurtosis-0.81988192
Mean12.411813
Median Absolute Deviation (MAD)5
Skewness0.26602051
Sum686547
Variance40.998578
MonotonicityNot monotonic
2023-11-19T16:58:08.753858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9 3639
 
6.6%
10 3578
 
6.5%
8 3411
 
6.2%
11 3261
 
5.9%
12 3016
 
5.5%
7 2988
 
5.4%
13 2672
 
4.8%
6 2650
 
4.8%
14 2395
 
4.3%
5 2240
 
4.0%
Other values (17) 25464
46.0%
ValueCountFrequency (%)
0 410
 
0.7%
1 799
 
1.4%
2 1257
 
2.3%
3 1507
2.7%
4 1786
3.2%
5 2240
4.0%
6 2650
4.8%
7 2988
5.4%
8 3411
6.2%
9 3639
6.6%
ValueCountFrequency (%)
26 715
 
1.3%
25 1040
1.9%
24 1254
2.3%
23 1526
2.8%
22 1609
2.9%
21 1777
3.2%
20 1785
3.2%
19 1828
3.3%
18 1927
3.5%
17 2049
3.7%

training_count_last_28_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1171
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.107694
Minimum0
Maximum7443
Zeros19207
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:09.428138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q363
95-th percentile215
Maximum7443
Range7443
Interquartile range (IQR)63

Descriptive statistics

Standard deviation188.28828
Coefficient of variation (CV)3.0812533
Kurtosis210.51782
Mean61.107694
Median Absolute Deviation (MAD)11
Skewness11.300982
Sum3380111
Variance35452.478
MonotonicityNot monotonic
2023-11-19T16:58:10.025138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19207
34.7%
1 1496
 
2.7%
2 1220
 
2.2%
3 1073
 
1.9%
4 900
 
1.6%
5 741
 
1.3%
6 706
 
1.3%
8 596
 
1.1%
7 586
 
1.1%
9 580
 
1.0%
Other values (1161) 28209
51.0%
ValueCountFrequency (%)
0 19207
34.7%
1 1496
 
2.7%
2 1220
 
2.2%
3 1073
 
1.9%
4 900
 
1.6%
5 741
 
1.3%
6 706
 
1.3%
7 586
 
1.1%
8 596
 
1.1%
9 580
 
1.0%
ValueCountFrequency (%)
7443 1
< 0.1%
6866 1
< 0.1%
5349 1
< 0.1%
4943 1
< 0.1%
4883 1
< 0.1%
4795 1
< 0.1%
4607 1
< 0.1%
4130 1
< 0.1%
4078 1
< 0.1%
3917 1
< 0.1%

global_competition_level
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6632317
Minimum0
Maximum11
Zeros17551
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2023-11-19T16:58:10.661586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8289278
Coefficient of variation (CV)1.0622162
Kurtosis-0.51547738
Mean2.6632317
Median Absolute Deviation (MAD)1
Skewness0.8380647
Sum147314
Variance8.0028326
MonotonicityNot monotonic
2023-11-19T16:58:11.071583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 17551
31.7%
1 10389
18.8%
2 4898
 
8.9%
3 3965
 
7.2%
5 3701
 
6.7%
6 3651
 
6.6%
7 3458
 
6.3%
4 3409
 
6.2%
8 2374
 
4.3%
9 1327
 
2.4%
Other values (2) 591
 
1.1%
ValueCountFrequency (%)
0 17551
31.7%
1 10389
18.8%
2 4898
 
8.9%
3 3965
 
7.2%
4 3409
 
6.2%
5 3701
 
6.7%
6 3651
 
6.6%
7 3458
 
6.3%
8 2374
 
4.3%
9 1327
 
2.4%
ValueCountFrequency (%)
11 136
 
0.2%
10 455
 
0.8%
9 1327
 
2.4%
8 2374
4.3%
7 3458
6.3%
6 3651
6.6%
5 3701
6.7%
4 3409
6.2%
3 3965
7.2%
2 4898
8.9%

tokens_spent_last_28_days
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1530
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.3939
Minimum0
Maximum120233
Zeros18799
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:11.574935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3107
95-th percentile361
Maximum120233
Range120233
Interquartile range (IQR)107

Descriptive statistics

Standard deviation842.77553
Coefficient of variation (CV)6.9424868
Kurtosis8938.5575
Mean121.3939
Median Absolute Deviation (MAD)38
Skewness75.327708
Sum6714782
Variance710270.59
MonotonicityNot monotonic
2023-11-19T16:58:12.185551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18799
34.0%
5 712
 
1.3%
2 526
 
1.0%
10 386
 
0.7%
20 336
 
0.6%
15 278
 
0.5%
50 275
 
0.5%
25 264
 
0.5%
13 261
 
0.5%
4 259
 
0.5%
Other values (1520) 33218
60.1%
ValueCountFrequency (%)
0 18799
34.0%
1 158
 
0.3%
2 526
 
1.0%
3 244
 
0.4%
4 259
 
0.5%
5 712
 
1.3%
6 258
 
0.5%
7 209
 
0.4%
8 239
 
0.4%
9 204
 
0.4%
ValueCountFrequency (%)
120233 1
< 0.1%
76236 1
< 0.1%
37698 1
< 0.1%
33731 1
< 0.1%
28382 1
< 0.1%
27111 1
< 0.1%
26207 1
< 0.1%
25805 1
< 0.1%
25169 1
< 0.1%
23180 1
< 0.1%

tokens_stash
Real number (ℝ)

SKEWED  ZEROS 

Distinct1642
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.13308
Minimum-4975
Maximum744202
Zeros1087
Zeros (%)2.0%
Negative8
Negative (%)< 0.1%
Memory size432.3 KiB
2023-11-19T16:58:12.748746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4975
5-th percentile2
Q111
median29
Q373
95-th percentile352
Maximum744202
Range749177
Interquartile range (IQR)62

Descriptive statistics

Standard deviation3183.5504
Coefficient of variation (CV)28.139873
Kurtosis53957.111
Mean113.13308
Median Absolute Deviation (MAD)22
Skewness230.87115
Sum6257843
Variance10134993
MonotonicityNot monotonic
2023-11-19T16:58:13.294743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1288
 
2.3%
10 1282
 
2.3%
4 1221
 
2.2%
7 1199
 
2.2%
11 1185
 
2.1%
3 1177
 
2.1%
8 1138
 
2.1%
5 1133
 
2.0%
6 1114
 
2.0%
2 1111
 
2.0%
Other values (1632) 43466
78.6%
ValueCountFrequency (%)
-4975 1
 
< 0.1%
-629 1
 
< 0.1%
-391 1
 
< 0.1%
-342 2
 
< 0.1%
-275 1
 
< 0.1%
-196 1
 
< 0.1%
-10 1
 
< 0.1%
0 1087
2.0%
1 1021
1.8%
2 1111
2.0%
ValueCountFrequency (%)
744202 1
< 0.1%
20424 1
< 0.1%
16750 1
< 0.1%
14375 1
< 0.1%
12705 1
< 0.1%
12252 1
< 0.1%
10711 1
< 0.1%
10684 1
< 0.1%
9942 1
< 0.1%
7345 1
< 0.1%

rests_stash
Real number (ℝ)

HIGH CORRELATION 

Distinct2686
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.08352
Minimum-169
Maximum32767
Zeros514
Zeros (%)0.9%
Negative1
Negative (%)< 0.1%
Memory size432.3 KiB
2023-11-19T16:58:13.824747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-169
5-th percentile8
Q141
median88
Q3243
95-th percentile1054
Maximum32767
Range32936
Interquartile range (IQR)202

Descriptive statistics

Standard deviation789.25096
Coefficient of variation (CV)2.7880498
Kurtosis319.29357
Mean283.08352
Median Absolute Deviation (MAD)65
Skewness13.034028
Sum15658482
Variance622917.08
MonotonicityNot monotonic
2023-11-19T16:58:14.346742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 1216
 
2.2%
43 1007
 
1.8%
0 514
 
0.9%
52 504
 
0.9%
44 499
 
0.9%
45 449
 
0.8%
3 417
 
0.8%
24 401
 
0.7%
46 400
 
0.7%
53 398
 
0.7%
Other values (2676) 49509
89.5%
ValueCountFrequency (%)
-169 1
 
< 0.1%
0 514
0.9%
1 155
 
0.3%
2 146
 
0.3%
3 417
0.8%
4 295
0.5%
5 280
0.5%
6 298
0.5%
7 376
0.7%
8 314
0.6%
ValueCountFrequency (%)
32767 1
< 0.1%
32553 1
< 0.1%
32185 1
< 0.1%
30757 1
< 0.1%
27677 1
< 0.1%
23232 1
< 0.1%
20725 1
< 0.1%
19436 1
< 0.1%
18012 1
< 0.1%
17908 1
< 0.1%

morale_boosters_stash
Real number (ℝ)

HIGH CORRELATION 

Distinct2805
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.08596
Minimum0
Maximum32767
Zeros313
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:14.864743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q152
median118
Q3281
95-th percentile1116
Maximum32767
Range32767
Interquartile range (IQR)229

Descriptive statistics

Standard deviation823.36249
Coefficient of variation (CV)2.6812117
Kurtosis524.00294
Mean307.08596
Median Absolute Deviation (MAD)82
Skewness17.344588
Sum16986153
Variance677925.78
MonotonicityNot monotonic
2023-11-19T16:58:15.527744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 360
 
0.7%
40 354
 
0.6%
42 336
 
0.6%
38 333
 
0.6%
37 331
 
0.6%
50 322
 
0.6%
44 322
 
0.6%
51 321
 
0.6%
36 318
 
0.6%
53 318
 
0.6%
Other values (2795) 51999
94.0%
ValueCountFrequency (%)
0 313
0.6%
1 208
0.4%
2 214
0.4%
3 176
0.3%
4 204
0.4%
5 183
0.3%
6 195
0.4%
7 190
0.3%
8 225
0.4%
9 193
0.3%
ValueCountFrequency (%)
32767 2
< 0.1%
32765 2
< 0.1%
32758 1
< 0.1%
32713 1
< 0.1%
32587 1
< 0.1%
32451 1
< 0.1%
31583 1
< 0.1%
26076 1
< 0.1%
25496 1
< 0.1%
24982 1
< 0.1%

league_rank
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.3 KiB
2023-11-19T16:58:15.960744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7.5
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0311653
Coefficient of variation (CV)0.53748871
Kurtosis-1.2123088
Mean7.5
Median Absolute Deviation (MAD)3.5
Skewness0
Sum414855
Variance16.250294
MonotonicityNot monotonic
2023-11-19T16:58:16.375746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 3951
 
7.1%
1 3951
 
7.1%
6 3951
 
7.1%
10 3951
 
7.1%
12 3951
 
7.1%
7 3951
 
7.1%
5 3951
 
7.1%
8 3951
 
7.1%
3 3951
 
7.1%
4 3951
 
7.1%
Other values (4) 15804
28.6%
ValueCountFrequency (%)
1 3951
7.1%
2 3951
7.1%
3 3951
7.1%
4 3951
7.1%
5 3951
7.1%
6 3951
7.1%
7 3951
7.1%
8 3951
7.1%
9 3951
7.1%
10 3951
7.1%
ValueCountFrequency (%)
14 3951
7.1%
13 3951
7.1%
12 3951
7.1%
11 3951
7.1%
10 3951
7.1%
9 3951
7.1%
8 3951
7.1%
7 3951
7.1%
6 3951
7.1%
5 3951
7.1%

continent
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size432.3 KiB
Europe
22468 
Asia
22114 
South America
5408 
Africa
3030 
North America
 
2045
Other values (2)
 
249

Length

Max length13
Median length7
Mean length6.1480999
Min length4

Characters and Unicode

Total characters340076
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEurope
2nd rowAsia
3rd rowEurope
4th rowAsia
5th rowEurope

Common Values

ValueCountFrequency (%)
Europe 22468
40.6%
Asia 22114
40.0%
South America 5408
 
9.8%
Africa 3030
 
5.5%
North America 2045
 
3.7%
Oceania 218
 
0.4%
Unknown 31
 
0.1%

Length

2023-11-19T16:58:16.806746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T16:58:17.209743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
europe 22468
35.8%
asia 22114
35.2%
america 7453
 
11.9%
south 5408
 
8.6%
africa 3030
 
4.8%
north 2045
 
3.3%
oceania 218
 
0.3%
unknown 31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 34996
10.3%
a 33033
9.7%
i 32815
9.6%
A 32597
9.6%
e 30139
8.9%
o 29952
8.8%
u 27876
8.2%
E 22468
6.6%
p 22468
6.6%
s 22114
6.5%
Other values (13) 51618
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 269856
79.4%
Uppercase Letter 62767
 
18.5%
Space Separator 7453
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 34996
13.0%
a 33033
12.2%
i 32815
12.2%
e 30139
11.2%
o 29952
11.1%
u 27876
10.3%
p 22468
8.3%
s 22114
8.2%
c 10701
 
4.0%
m 7453
 
2.8%
Other values (6) 18309
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 32597
51.9%
E 22468
35.8%
S 5408
 
8.6%
N 2045
 
3.3%
O 218
 
0.3%
U 31
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332623
97.8%
Common 7453
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 34996
10.5%
a 33033
9.9%
i 32815
9.9%
A 32597
9.8%
e 30139
9.1%
o 29952
9.0%
u 27876
8.4%
E 22468
6.8%
p 22468
6.8%
s 22114
6.6%
Other values (12) 44165
13.3%
Common
ValueCountFrequency (%)
7453
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 34996
10.3%
a 33033
9.7%
i 32815
9.6%
A 32597
9.6%
e 30139
8.9%
o 29952
8.8%
u 27876
8.2%
E 22468
6.6%
p 22468
6.6%
s 22114
6.5%
Other values (13) 51618
15.2%

Interactions

2023-11-19T16:57:38.877400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:54:57.540389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:06.154389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:14.168387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:22.681391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:31.016387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:38.973882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:48.699220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:58.479274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:08.185272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:16.842659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:25.819684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:35.968831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:45.247829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:54.426493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:03.269114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:11.679613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:20.687552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:29.857718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:39.355556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:54:58.015390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:06.587389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:14.612389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:23.105386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:31.426390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:39.398881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:49.202274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:59.130274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:08.653275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:17.260659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:26.283413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:36.460831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:45.836831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:54.894858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:03.763351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:12.154269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:21.159641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:30.335758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:39.725871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:54:58.500388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:06.971388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:14.993389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:23.454389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:31.945388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:39.770881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:49.672275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:59.695273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:09.079275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:17.611660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:26.666336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:36.911830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:46.269831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:55.375858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:04.125040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:12.509268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:21.548699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:30.725632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:40.143661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-11-19T16:56:05.261275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:14.332659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:23.052218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:32.363575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:42.466830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:51.691573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:00.659040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:09.107632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:17.964855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:27.114750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:36.065581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:45.108278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:03.956390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:12.184388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:20.694387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:28.864389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:36.967885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:46.035421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:56.063274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:05.694273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:14.713659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:23.510459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:33.675643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:43.044829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:52.122952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:01.132792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:09.510494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:18.446165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:27.517356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:36.533538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:45.561061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:04.397388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:12.576388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:21.056388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:29.294389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:37.402884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:46.730423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:56.562275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:06.254271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:15.155658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:24.016167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:34.114145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:43.488830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:52.553532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:01.572330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:09.902026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:18.942192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:28.114951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:36.925233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:46.067070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:04.836387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:12.954390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:21.438387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:29.709390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:37.785885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:47.333423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:57.001274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:06.799274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:15.547659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:24.487335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:34.562830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:43.903831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:53.092532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:02.046451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:10.318688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:19.360389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:28.517504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:37.380588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:46.501721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:05.248389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:13.323389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:21.801390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:30.170390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:38.195883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:47.810835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:57.476272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:07.323273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:15.925660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:24.930191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:34.949832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:44.320831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:53.566244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:02.437094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:10.719680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:19.776587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:28.934043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:37.893855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:46.916145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:05.628391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:13.738389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:22.248388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:30.579386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:38.597882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:48.267615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:55:57.989273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:07.755274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:16.383659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:25.385366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:35.445829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:44.728831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:56:53.966246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:02.883515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:11.136333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:20.179053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:29.333982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T16:57:38.436918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-19T16:58:18.593742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
club_idleague_idcohort_seasonavg_age_top_11_playersavg_stars_top_11_playersavg_stars_top_14_playersavg_training_factor_top_11_playersdays_active_last_28_daysleague_match_watched_count_last_28_dayssession_count_last_28_daysplaytime_last_28_daysleague_match_won_count_last_28_daystraining_count_last_28_daysglobal_competition_leveltokens_spent_last_28_daystokens_stashrests_stashmorale_boosters_stashleague_rankdynamic_payment_segmentregistration_platform_specificcontinent
club_id1.000-0.724-0.9870.003-0.496-0.498-0.107-0.483-0.364-0.415-0.366-0.139-0.385-0.537-0.259-0.197-0.242-0.1960.0870.2550.2610.123
league_id-0.7241.0000.679-0.0990.6630.667-0.2020.4980.3650.4490.4180.0170.4350.4710.3760.2740.4540.3540.0000.2240.2180.117
cohort_season-0.9870.6791.0000.0190.4730.4740.1390.4840.3650.4160.3670.1400.3850.5290.2560.1840.2010.154-0.0910.2440.3210.115
avg_age_top_11_players0.003-0.0990.0191.000-0.115-0.110-0.047-0.150-0.155-0.176-0.182-0.170-0.215-0.157-0.191-0.143-0.100-0.1440.1220.0730.0570.029
avg_stars_top_11_players-0.4960.6630.473-0.1151.0000.998-0.4330.7460.6420.7510.7420.2420.7370.5800.7030.1910.2040.228-0.3110.2980.1120.084
avg_stars_top_14_players-0.4980.6670.474-0.1100.9981.000-0.4440.7460.6430.7500.7410.2380.7350.5770.6980.1910.2020.224-0.3100.3020.1200.088
avg_training_factor_top_11_players-0.107-0.2020.139-0.047-0.433-0.4441.000-0.261-0.204-0.273-0.2890.039-0.218-0.086-0.3600.122-0.057-0.0580.1250.0650.0290.027
days_active_last_28_days-0.4830.4980.484-0.1500.7460.746-0.2611.0000.8260.9690.9450.5290.8900.5960.8210.1020.0050.065-0.5140.2190.0950.075
league_match_watched_count_last_28_days-0.3640.3650.365-0.1550.6420.643-0.2040.8261.0000.8630.8620.5480.8010.5370.7280.073-0.0600.019-0.5110.2030.0510.040
session_count_last_28_days-0.4150.4490.416-0.1760.7510.750-0.2730.9690.8631.0000.9790.5540.9140.6010.8480.087-0.0160.063-0.5390.1810.0330.031
playtime_last_28_days-0.3660.4180.367-0.1820.7420.741-0.2890.9450.8620.9791.0000.5420.9250.5890.8630.060-0.0470.037-0.5400.1310.0290.009
league_match_won_count_last_28_days-0.1390.0170.140-0.1700.2420.2380.0390.5290.5480.5540.5421.0000.5200.3750.465-0.023-0.184-0.019-0.4640.1410.0430.021
training_count_last_28_days-0.3850.4350.385-0.2150.7370.735-0.2180.8900.8010.9140.9250.5201.0000.5970.8210.102-0.0340.058-0.5080.0530.0160.000
global_competition_level-0.5370.4710.529-0.1570.5800.577-0.0860.5960.5370.6010.5890.3750.5971.0000.5220.1080.0690.158-0.3000.1970.0630.064
tokens_spent_last_28_days-0.2590.3760.256-0.1910.7030.698-0.3600.8210.7280.8480.8630.4650.8210.5221.000-0.003-0.0320.059-0.4750.0720.0090.000
tokens_stash-0.1970.2740.184-0.1430.1910.1910.1220.1020.0730.0870.060-0.0230.1020.108-0.0031.0000.4830.3710.0040.0420.0250.000
rests_stash-0.2420.4540.201-0.1000.2040.202-0.0570.005-0.060-0.016-0.047-0.184-0.0340.069-0.0320.4831.0000.6780.1460.0440.0560.017
morale_boosters_stash-0.1960.3540.154-0.1440.2280.224-0.0580.0650.0190.0630.037-0.0190.0580.1580.0590.3710.6781.0000.0460.0540.0490.015
league_rank0.0870.000-0.0910.122-0.311-0.3100.125-0.514-0.511-0.539-0.540-0.464-0.508-0.300-0.4750.0040.1460.0461.0000.1120.0230.011
dynamic_payment_segment0.2550.2240.2440.0730.2980.3020.0650.2190.2030.1810.1310.1410.0530.1970.0720.0420.0440.0540.1121.0000.1800.102
registration_platform_specific0.2610.2180.3210.0570.1120.1200.0290.0950.0510.0330.0290.0430.0160.0630.0090.0250.0560.0490.0230.1801.0000.126
continent0.1230.1170.1150.0290.0840.0880.0270.0750.0400.0310.0090.0210.0000.0640.0000.0000.0170.0150.0110.1020.1261.000

Missing values

2023-11-19T16:57:47.528501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-19T16:57:49.055048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

seasonclub_idleague_iddynamic_payment_segmentcohort_seasonavg_age_top_11_playersavg_stars_top_11_playersavg_stars_top_14_playersavg_training_factor_top_11_playersdays_active_last_28_daysleague_match_watched_count_last_28_dayssession_count_last_28_daysplaytime_last_28_daysregistration_countryregistration_platform_specificleague_match_won_count_last_28_daystraining_count_last_28_daysglobal_competition_leveltokens_spent_last_28_daystokens_stashrests_stashmorale_boosters_stashleague_rankcontinent
0173604282529123482) Minnow134255.2551514.4830091.778801251027181281642PortugalFlash FB Canvas24435115982451868182Europe
1173962096729121402) Minnow28234.9625214.785648-0.00268728911958448442TurkeyAndroid Phone19587269035242Asia
2173604547429129334) Whale1362311.10578210.5111330.6287942822210134640047BelgiumFlash FB Canvas2611682318036944449331Europe
31731418777329103710) NonPayer2214.1648484.0234860.5519041201812143308MalaysiaAndroid Phone1122241432131266Asia
41731369485329076320) NonPayer5223.0319392.9260290.6905440000ItalyiOS Phone90401610014710Europe
5173599076229125480) NonPayer104235.1654915.0149620.50869128611352316063ItalyiOS Phone186631173252282Europe
61731422364229112992) Minnow2235.3316975.1655140.2606032511176115438831CambodiaiOS Phone2110563131948272Asia
71731170950729103710) NonPayer16243.8812363.7276570.5877861112438713IndonesiaAndroid Phone12209259718112Asia
81731449295029059190) NonPayer1274.3078794.0787240.442616752515506003AlgeriaAndroid Phone62217846667Africa
91731443685329063830) NonPayer1223.7577823.5367620.6111143095814384SpainAndroid Phone14120473931665Europe
seasonclub_idleague_iddynamic_payment_segmentcohort_seasonavg_age_top_11_playersavg_stars_top_11_playersavg_stars_top_14_playersavg_training_factor_top_11_playersdays_active_last_28_daysleague_match_watched_count_last_28_dayssession_count_last_28_daysplaytime_last_28_daysregistration_countryregistration_platform_specificleague_match_won_count_last_28_daystraining_count_last_28_daysglobal_competition_leveltokens_spent_last_28_daystokens_stashrests_stashmorale_boosters_stashleague_rankcontinent
553041731428847129073552) Minnow2254.6790424.6123710.439115265142108773302BrazilAndroid Phone2454114742372South America
553051731237648629123920) NonPayer11254.8061334.6433910.489918281016264578060JordanAndroid Phone17811522817104Asia
553061731433573529087470) NonPayer1234.5651154.4465710.4218213052171933FranceiOS Tablet29090402792255Europe
553071731439038529084040) NonPayer1234.8981824.7595430.4145041024720124926EgyptAndroid Phone11601135881955Africa
553081731383021629107580) NonPayer4254.4702184.1677330.3235810000FranceiOS Phone4000181016387Europe
553091731427814529065140) NonPayer2254.1609093.9480860.5032411122818536739SwitzerlandiOS Phone13250421025371Europe
553101731430955629069132) Minnow2214.2441453.7622860.6645142825383319296693FranceiOS Phone2516702141583582561Europe
553111731445565429059850) NonPayer1214.0001583.7532760.507620622710648296FranceiOS Tablet141707311705711Europe
553121731362373329104370) NonPayer5244.5821584.4613910.664767281312170077991MoroccoAndroid Phone2280490651091505Africa
553131731171997129103810) NonPayer16235.1765824.7623050.640298101604376GreeceAndroid Phone6265024173549Europe